Workflow Templates

KB Builder — Historical Emails n8n Workflow Template 📘 Description This workflow automates the process of building a structured Knowledge Base (KB) from your existing Gmail inbox by processing historical email threads, extracting customer-support conversation pairs, classifying them with AI, generating vector embeddings, and storing everything in a PostgreSQL database — ready to power AI-assisted draft generation in downstream workflows. The workflow is triggered manually and fetches the last N emails from your connected Gmail account. Each email is parsed, filtered, and deduplicated before being processed. A thread-level fetch retrieves the full conversation context, separating customer messages from support replies. An AI classification step categorises each thread into a defined scenario type. Vector embeddings are generated for semantic similarity search. Qualified threads are inserted into three database tables — kb_data for clean Q&A pairs, scenario_patterns for reusable handling logic, and corrections for diverse real-world examples — with strict deduplication to keep the KB clean and non-redundant. ⚙️ What This Workflow Does (Step-by-Step) 📥 Manual Trigger — Run on demand whenever you want to import a batch of historical emails into the KB. No scheduling required. 📬 Fetch Emails from Gmail — Pulls the last N emails (configurable, default 100) from your Gmail account using OAuth2. Returns full email metadata including thread IDs, sender, subject, labels, and body. 🔍 Parse & Filter — Extracts structured fields from each email: sender address, subject, body (cleaned of links and quoted history), date, and thread ID. Filters out emails from your own domain (outbound support replies) and auto-generated emails (no-reply, notifications, newsletters). Only genuine inbound customer emails proceed. 🧵 Fetch Full Thread — For each qualified email, fetches the entire Gmail thread using the thread ID. Retrieves all messages in the conversation to identify the original customer message and the latest support reply. 🗂️ Assemble Thread Context — Reconstructs the conversation by separating customer messages from support replies. Builds a clean conversationText block used as AI input. Flags threads with no support reply for conditional handling. 🤖 AI Classification — Calls GPT-4o-mini (or Claude) with the assembled conversation. Returns a structured JSON output containing: scenario category, a concise Q&A pair, a handling pattern description, key entities, sentiment, resolution status, and a summary. 🔢 Generate Embeddings — Calls the OpenAI Embeddings API to generate a 1536-dimension vector for both the KB entry and the correction record. Used for semantic similarity search in the downstream draft-generation workflow. 🔁 Duplicate Detection & DB Insert — Performs cosine similarity checks against existing records before inserting. KB entries and scenario patterns are blocked if a match exceeds 92% similarity. Corrections are inserted freely (deduped at 92%) since diverse examples improve AI draft quality. New records are written to three tables: kb_data, scenario_patterns, and corrections. 🧩 Prerequisites Gmail account** — OAuth2 credentials connected in n8n. The account must be the support inbox you want to import from. OpenAI API key** — Used for GPT-4o-mini classification ($0.002–0.005 per thread) and text-embedding-3-small for vector generation ($0.0001 per record). PostgreSQL database** — With the pgvector extension enabled. Must have the three tables set up per the schema below. n8n instance** — Self-hosted or cloud. Requires the PostgreSQL and OpenAI nodes. 🗄️ Database Schema Table: kb_data | Field | Type | Notes | |---|---|---| | id | SERIAL PRIMARY KEY | Auto-increment | | thread_id | TEXT | Gmail thread ID | | subject | TEXT | Email subject | | category | TEXT | AI-assigned scenario type | | question | TEXT | Customer issue / question | | answer | TEXT | Support resolution | | entities | TEXT | Key entities extracted by AI | | sentiment | TEXT | Customer sentiment | | resolution_status | TEXT | Resolved / Unresolved | | embedding | vector(1536) | OpenAI embedding for similarity search | | source | TEXT | historical_import | | created_at | TIMESTAMP | Insert timestamp | Table: scenario_patterns | Field | Type | Notes | |---|---|---| | id | SERIAL PRIMARY KEY | Auto-increment | | category | TEXT | Scenario type | | pattern_description | TEXT | Handling logic summary | | example_subject | TEXT | Representative subject line | | embedding | vector(1536) | OpenAI embedding | | source | TEXT | historical_import | | created_at | TIMESTAMP | Insert timestamp | Table: corrections | Field | Type | Notes | |---|---|---| | id | SERIAL PRIMARY KEY | Auto-increment | | gmail_thread_id | TEXT | Gmail thread ID | | original_email_body | TEXT | Customer's original message | | human_sent_text | TEXT | Actual support reply sent | | ai_draft_text | TEXT | NULL for historical imports | | diff_summary | TEXT | Import note or live diff | | classification | TEXT | Scenario category | | embedding | vector(1536) | OpenAI embedding | | source | TEXT | historical_import | | created_at | TIMESTAMP | Insert timestamp | 💰 Cost Estimate | Item | Estimated Cost | |---|---| | Gmail OAuth2 | Free | | GPT-4o-mini (100 threads classification) | ~$0.20–0.50 | | text-embedding-3-small (100 records × 2 embeddings) | ~$0.02 | | PostgreSQL + pgvector (self-hosted) | ~$5–15/mo | | n8n self-hosted (AWS t3.small) | ~$10–15/mo | | Total per 100-email import run | ~$0.25–0.75 | ⚙️ Setup Instructions Gmail — Connect your Gmail account in n8n using OAuth2. Ensure the account is the support inbox. Grant read permissions for messages and threads. PostgreSQL — Create a new database, enable the pgvector extension (CREATE EXTENSION IF NOT EXISTS vector), then create the three tables using the schema above. OpenAI — Add your OpenAI API key as an n8n credential. Used for both the chat completion node (classification) and the HTTP Request node (embeddings). Customise the AI prompt — Open the AI Classification node and update the system prompt to reflect your business type, support tone, and the scenario categories relevant to your domain (e.g. Refund Request, Order Status, Technical Issue, Billing Query). Set your domain filter — In the Parse & Filter node, update the YOUR_DOMAIN variable to your support team's email domain so outbound replies are correctly excluded from customer email processing. Set fetch limit — In the Fetch Emails node, set the limit parameter to the number of historical emails you want to import per run. Start with 5–10 to validate the pipeline before running at scale. Run manually — Click Execute Workflow. Monitor the output of each node to verify parsing, classification, and DB inserts are working correctly. 💡 Key Benefits ✔ Converts your inbox into a structured KB — no manual tagging or categorisation required ✔ AI classification assigns scenario types and extracts Q&A pairs automatically ✔ Vector embeddings enable semantic similarity search in downstream draft workflows ✔ Smart deduplication keeps KB and scenario tables clean — no near-duplicate entries ✔ Corrections table accumulates diverse examples — improves AI draft quality over time ✔ Thread-aware — reconstructs full conversations, not just individual emails ✔ Modular AI node — swap GPT-4o-mini for Claude or any other model with minimal changes ✔ One-time historical import feeds directly into live draft-generation workflows 👥 Perfect For Support teams wanting to automate email draft generation with AI trained on their own history Founders or small teams building a knowledge base from years of accumulated support emails Developers building AI-powered support automation on top of Gmail Anyone who wants their AI drafts to sound like their own team — not generic templates 🔗 Related Workflows This workflow is Step 1 in a two-workflow system: KB Builder — Historical Emails* *(this workflow) — Imports historical threads to seed the KB AI Draft Generator* *(coming soon) — Monitors new inbound emails, retrieves semantically similar KB entries and corrections, and generates personalised reply drafts delivered to your inbox for one-click review and send

Who's this for Finance teams, AI developers, product managers, and business owners who need to monitor and control OpenAI API costs across different models and projects. If you're using GPT-4, GPT-3.5, or other OpenAI models and want to track spending patterns, identify cost optimization opportunities, and generate stakeholder reports, this workflow is for you. What it does This workflow automatically tracks your OpenAI token usage on a monthly basis, breaks down costs by model and date, stores the data in Google Sheets with automatic cost calculations, and emails PDF reports to stakeholders. It transforms raw API usage data into actionable insights, helping you understand which models are driving costs, identify usage trends over time, and maintain budget accountability. The workflow runs completely hands-free once configured, generating comprehensive monthly reports without manual intervention. How it works The workflow executes automatically on the 5th of each month and follows these steps: Creates a new Google Sheet from your template with the naming format "Token_Tracking_[Month]_[Year]" Fetches the previous month's OpenAI usage data via the OpenAI Admin API Transforms raw API responses into a clean daily breakdown showing usage by model Appends the data to Google Sheets with columns for date, model, input tokens, and output tokens Your Google Sheets formulas automatically calculate costs based on OpenAI's pricing for each model Exports the completed report as both PDF and Excel formats Emails the PDF report to designated stakeholders with a summary message Archives the Excel file to Google Drive for long-term recordkeeping and historical analysis Requirements OpenAI account with Admin API access (required to access organization usage endpoints) Google Sheets template pre-configured with cost calculation formulas Google Drive for report storage and archiving Gmail account for sending email notifications n8n instance (self-hosted or cloud) with the following credentials configured: OpenAI API credentials Google Sheets OAuth2 Google Drive OAuth2 Gmail OAuth2 Setup instructions Create your Google Sheets template Set up a Google Sheet with these columns: Date Model Token Usage In Token Usage Out Token Cost Input (formula: =C2 * [price per 1M input tokens] / 1000000) Token Cost Output (formula: =D2 * [price per 1M output tokens] / 1000000) Total Cost USD (formula: =E2 + F2) Total Cost AUD (optional, formula: =G2 * [exchange rate]) (workflow contains a template) Include pricing formulas based on OpenAI's current pricing. Add summary calculations at the bottom to total costs by model. 2. Configure n8n credentials In your n8n instance, set up credentials for: OpenAI API (you'll need admin access to your organization) Google Sheets (OAuth2 connection) Google Drive (OAuth2 connection) Gmail (OAuth2 connection) 3. Update workflow placeholders Replace the following placeholders in the workflow: your-api-key-id: Your OpenAI API key ID (find this in your OpenAI dashboard) your-template-file-id: The ID of your Google Sheets template your-archive-folder-id: The Google Drive folder ID where reports should be archived your-email@example.com: The email address that should receive monthly reports 4. Assign credentials to nodes Open each node that requires credentials and select the appropriate credential from your configured options: "Fetch OpenAI Usage Data" → OpenAI API credential "Append Data to Google Sheet" → Google Sheets credential "Create Monthly Report from Template" → Google Drive credential "Export Sheet as Excel" → Google Drive credential "Export Sheet as PDF for Email" → Google Drive credential "Archive Report to Drive" → Google Drive credential "Email Report to Stakeholder" → Gmail credential 5. Test the workflow Before enabling the schedule, manually execute the workflow to ensure: The template copies successfully OpenAI data fetches correctly Data appends to the sheet properly PDF and Excel exports work Email sends successfully File archives to the correct folder 6. Enable the schedule Once testing is complete, activate the workflow. It will run automatically on the 5th of each month.

Team Wellness - AI Burnout Detector Agent devex github 🎯 Demo sample report github action code alternative How it works 🎯 Overview A comprehensive n8n workflow that analyzes developer workload patterns from GitHub repositories to detect potential software engineering team burnout risks and provide actionable team wellness recommendations. This workflow automatically monitors team activity patterns, analyzes them using AI, and provides professional wellness reports with actionable recommendations which will automate GitHub issue creation and do email notifications for critical alerts. ✨ Features Automated Data Collection**: Fetches commits, pull requests, and workflow data from GitHub Pattern Analysis**: Identifies late-night work, weekend activity, and workload distribution AI-Powered Analysis**: Uses Groq's LLM for professional burnout risk assessment Automated Actions**: Creates GitHub issues and sends email alerts based on criticality Professional Guardrails**: Ensures objective, evidence-based analysis with privacy protection Scheduled Monitoring**: Weekly automated wellness checks 🏗️ Architecture 1. Data Collection Layer GitHub Commits API**: Fetches commit history and timing data GitHub Pull Requests API**: Analyzes collaboration patterns GitHub Workflows API**: Monitors CI/CD pipeline health 2. Pattern Analysis Engine Work Pattern Signals**: Late-night commits, weekend activity Developer Activity**: Individual contribution analysis Workflow Health**: Pipeline success/failure rates Collaboration Metrics**: PR review patterns and merge frequency 3. AI Analysis Layer Professional Guardrails**: Objective, evidence-based assessments Risk Assessment**: Burnout risk classification (Low/Medium/High) Health Scoring**: Team wellness score (0-100) Recommendation Engine**: Actionable suggestions for improvement 📊 Sample Output 📊 Team Health Report 📝 Summary Overall, the team is maintaining a healthy delivery pace, but there are emerging signs of workload imbalance due to increased after-hours activity. 🔢 Health Score Value:** 68 / 100 Confidence:** 87% Limitations:** Based solely on commit and PR activity; meeting load and non-code tasks not captured. 🔍 Observed Patterns ⏰ After-hours activity 29% of commits occurred between 10pm–1am (baseline: 12%). Confidence: 0.90 ⚠️ Systemic Risks Sustained after-hours work may indicate creeping burnout risk. Evidence: 3 consecutive weeks of elevated late-night commits. Confidence: 0.85 ✅ Recommendations 📌 Facilitate a team discussion on workload distribution and sprint commitments. (Priority: Medium) 🔔 Introduce automated nudges discouraging late-night commits. (Priority: Low) 🛠️ Rotate PR review responsibilities or adopt lightweight review guidelines. (Priority: High) 🚀 Quick Start Prerequisites n8n instance (cloud or self-hosted) GitHub repository with API access Groq API key Gmail account (optional, for email notifications) Setup Instructions Import Workflow Import the workflow JSON file into your n8n instance Configure Credentials GitHub API: Create a personal access token with repo access Groq API: Get your API key from Groq Console Gmail OAuth2: Set up OAuth2 credentials for email notifications Update Configuration { "repoowner": "your-github-username", "reponame": "your-repository-name", "period": 7, "emailreport": "your-email@company.com" } Test Workflow Run the workflow manually to verify all connections Check that data is being fetched correctly Verify AI analysis is working Schedule Automation Enable the schedule trigger for weekly reports Set up monitoring for critical alerts 🔧 Configuration Configuration Node Settings repoowner: GitHub username or organization reponame: Repository name period: Analysis period in days (default: 7) emailreport: Email address for critical alerts AI Model Settings Model**: openai/gpt-oss-120b (Groq) Temperature**: 0.3 (for consistent analysis) Max Tokens**: 2000 Safety Settings**: Professional content filtering 📈 Metrics Analyzed Repository-Level Metrics Total commits count Pull requests opened/closed Workflow runs and success rate Failed workflow percentage Work Pattern Signals Late-night commits (10PM-6AM) Weekend commits (Saturday-Sunday) Work intensity patterns Collaboration bottlenecks Developer-Level Activity Individual commit counts Late-night activity per developer Weekend activity per developer Workload distribution fairness 🛡️ Privacy & Ethics Professional Guardrails Never makes personal judgments about individual developers Only analyzes observable patterns in code activity data Always provides evidence-based reasoning for assessments Never suggests disciplinary actions or performance reviews Focuses on systemic issues and team-level recommendations Respects privacy and confidentiality of team members Data Protection No personal information is stored or transmitted Analysis is based solely on public repository data and public data All recommendations are constructive and team-focused Confidence scores indicate analysis reliability There is added redaction prompt. Note that LLM is not deterministic and usually, you will need to refine your own prompt to enhance difference level of criticality of privacy you need censored or displayed. In some cases ,you will need the engineer account names to help identify f2f conversation. 🔄 Workflow Nodes Core Nodes Schedule Trigger: Weekly automation (configurable) Config: Repository and email configuration Github Get Commits: Fetches commit history Github Get Workflows: Retrieves workflow runs Get Prs: Pulls pull request data Analyze Patterns Developer: JavaScript pattern analysis AI Agent: Groq-powered analysis with guardrails Update Github Issue: Creates wellness tracking issues Send a message in Gmail: Email notifications Data Flow Schedule Trigger → Config → Github APIs → Pattern Analysis → AI Agent → Actions 🚨 Alert Levels (Optional and Prompt configurable) Critical Alerts (Health Score < 90) GitHub Issue**: Automatic issue creation with detailed analysis Email Notification**: Immediate alert to team leads Slack Integration**: Critical team notifications Warning Alerts (Health Score 90-95) GitHub Issue**: Tracking issue for monitoring Slack Notification**: Team awareness message Normal Reports (Health Score > 95) Weekly Report**: Comprehensive team health summary Slack Summary**: Positive reinforcement message 🔧 Troubleshooting Common Issues GitHub API Rate Limits Solution: Use authenticated requests, implement rate limiting Check: API token permissions and repository access AI Analysis Failures Solution: Verify Groq API key, check model availability Check: Input data format and prompt structure Email Notifications Not Sending Solution: Verify Gmail OAuth2 setup, check email permissions Check: SMTP settings and authentication Workflow Execution Errors Solution: Check node connections, verify data flow Check: Error logs and execution history 🤝 Contributing Development Setup Fork the repository link above demo part Create a feature branch Make your changes Test thoroughly Submit a pull request Testing Test with different repository types Verify AI analysis accuracy Check alert threshold sensitivity Validate email and GitHub integrations 📄 License This project is licensed under the MIT License 🙏 Acknowledgments Groq**: For providing the AI analysis capabilities GitHub**: For the comprehensive API ecosystem n8n**: For the powerful workflow automation platform Community**: For feedback and contributions 📞 Support Getting Help Issues**: Create a GitHub issue for bugs or feature requests Discussions**: Use GitHub Discussions for questions Documentation**: Check the comprehensive setup guides Contact Email**:aiix.space.noreply@gmail.com LinkedIn**: SeanLon ⚠️ Important: This tool is designed for team wellness monitoring and should be used responsibly. Always respect team privacy and use the insights constructively to improve team health and productivity.

📄 PDF-to-Order Automation for Magento2 (Adobe commerce / open source) Description This n8n template demonstrates how to automatically process PDF purchase orders received via email and convert them into sales orders in Adobe Commerce (Magento 2) using Company Credit as the payment method. This is especially useful for B2B companies receiving structured orders from clients by email. Use cases include: Automating incoming B2B orders Reducing manual entry for sales teams Ensuring fast order creation in Adobe Commerce Reliable error handling and customer validation Good to know This workflow is tailored for Adobe Commerce, using the Company Credit payment method. It requires that the customer already has an account in Adobe Commerce and is authorized to use Company Credit. The same flow can be easily adapted for other payment methods (e.g. Purchase Order, Bank Transfer). No third-party services are required aside from n8n and access to Adobe Commerce with API credentials. How it works Trigger → Monitors an email inbox for incoming emails with PDF attachments. Extract PDF → Downloads the attached PDF and parses order data (e.g. SKU, quantity, customer reference). Validate Customer → Checks if the sender matches an existing customer in Adobe Commerce and verifies Company Credit eligibility. Create Order → Generates a new order in Magento using the extracted product data and Company Credit. Handle Errors → Logs issues and can notify a designated channel (email, Slack, etc.) if something goes wrong. Optional Enhancements → Add logging to Airtable, send confirmations to customers, or attach parsed order data to CRM entries. How to use A manual trigger is included as an example, but you can replace it with an IMAP Email Trigger, Gmail Trigger, or Webhook, depending on your setup. Customize the PDF parser node to fit your specific document layout and field structure. Configure Adobe Commerce API credentials in the HTTP nodes (or use environment variables). Optionally connect error steps to Slack, Email, or dashboards for monitoring. Requirements ✅ n8n instance (self-hosted or cloud) ✅ Adobe Commerce (Magento 2) instance with API access and Company Credit enabled ✅ Structured PDF templates used by your customers (Optional) Slack/Email/Airtable for notifications and logs Customising this workflow This workflow can be adapted for: Other payment methods (e.g. Purchase Orders, Online Payment) Magento open source ready. Just use your own payment method Alternate order sources (e.g. uploading PDFs via a portal instead of email) Parsing other document formats (e.g. CSV, Excel) Direct integration into ERP systems

Automated project status tracking with Airtable and Motion Who's it for Project managers, team leads, and agencies who need to automatically monitor project completion status across multiple clients and send notifications when specific milestones are reached. What it does This workflow automatically tracks project progress by connecting Airtable project databases with Motion task management. It monitors specific tasks within active projects and triggers email notifications when key milestones are completed. The system is designed to handle multiple projects simultaneously and can be customized for various notification triggers. How it works The workflow follows a structured monitoring process: Data Retrieval: Fetches project information from Airtable (project names and Motion workspace IDs) Motion Integration: Connects to Motion API using HTTP requests to retrieve project details Project Filtering: Identifies only active projects with "Todo" status containing "SEO" in the name Task Monitoring: Checks for specific completed tasks (e.g., "Intégrer les articles de blog") Conditional Notifications: Sends email alerts only when target tasks are marked as "Completed" Database Updates: Updates Airtable with last notification timestamps Requirements Airtable account with project database Motion account with API access Gmail account for email notifications HTTP request authentication for Motion API How to set up Step 1: Configure your Airtable database Ensure your Airtable contains the following fields: Project names: Names of projects to monitor Motion Workspace ID: Workspace identifiers for Motion API calls Status - Calendrier éditorial: Project status field (set to "Actif" for active projects) Last sent - Calendrier éditorial: Timestamp tracking for notification frequency Email addresses: Client and team member contact information Step 2: Set up API credentials Configure the following authentication in n8n: Airtable Personal Access Token: For database access Motion API: HTTP header authentication for Motion integration Gmail OAuth2: For email notification sending Step 3: Configure Motion API integration Base URL: Uses Motion API v1 endpoints Project retrieval: Fetches projects using workspace ID parameter Task monitoring: Searches for specific task names and completion status Custom filtering: Targets projects with "SEO" in name and "Todo" status Step 4: Customize scheduling Default schedule: Runs daily between 10th-31st of each month at 8 AM Cron expression: 0 8 10-31 * * (modify as needed) Frequency options: Can be adjusted for weekly, daily, or custom intervals Step 5: Set up email notifications Configure Gmail settings: Recipients: Project managers, clients, and collaborators Subject line: Dynamic formatting with project name and month Message template: HTML-formatted email with professional signature Sender name: Customizable organization name How to customize the workflow Single project, multiple tasks monitoring To adapt for monitoring one project with several different tasks: Modify the filter conditions to target your specific project Add multiple HTTP requests for different task names Create conditional branches for each task type Set up different notification templates per task Multi-project customization Database fields: Add custom fields in Airtable for different project types Filtering logic: Modify conditions to match your project categorization Motion workspace: Support multiple workspaces per client Notification rules: Set different notification frequencies per project Alternative notification methods Replace or complement Gmail with: Slack notifications: Send updates to team channels Discord integration: Alert development teams SMS notifications: Urgent milestone alerts Webhook integrations: Connect to custom internal systems Teams notifications: Enterprise communication Task monitoring variations Multiple task types: Monitor different milestones (design, development, testing) Task dependencies: Check completion of prerequisite tasks Progress tracking: Monitor task progress percentages Deadline monitoring: Alert on approaching deadlines Conditional logic features Smart filtering system Active project detection: Only processes projects marked as "Actif" Date-based filtering: Prevents duplicate notifications using timestamp comparison Status verification: Confirms task completion before sending notifications Project type filtering: Targets specific project categories (SEO projects in this example) Notification frequency control Monthly notifications: Prevents spam by tracking last sent dates Conditional execution: Only sends emails when tasks are actually completed Database updates: Automatically records notification timestamps Loop management: Processes multiple projects sequentially Results interpretation Automated monitoring outcomes Project status tracking: Real-time monitoring of active projects Milestone notifications: Immediate alerts when key tasks complete Database synchronization: Automatic updates of notification records Team coordination: Ensures all stakeholders are informed of progress Email notification content Each notification includes: Project identification: Clear project name and context Completion confirmation: Specific task that was completed Calendar reference: Links to editorial calendars or project resources Professional formatting: Branded email template with company signature Action items: Clear next steps for recipients Use cases Agency project management Client deliverable tracking: Monitor when content is ready for client review Milestone notifications: Alert teams when phases complete Quality assurance: Ensure all deliverables meet completion criteria Client communication: Automated updates on project progress Editorial workflow management Content publication: Track when articles are integrated into websites Editorial calendar: Monitor content creation and publication schedules Team coordination: Notify writers, editors, and publishers of status changes Client approval: Alert clients when content is ready for review Development project tracking Feature completion: Monitor when development milestones are reached Testing phases: Track QA completion and deployment readiness Client delivery: Automate notifications for UAT and launch phases Team synchronization: Keep all stakeholders informed of progress Workflow limitations Motion API dependency: Requires stable Motion API access and proper authentication Single task monitoring: Currently tracks one specific task type per execution Email-only notifications: Default setup uses Gmail (easily expandable) Monthly frequency: Designed for monthly notifications (customizable) Project naming dependency: Filters based on specific naming conventions Manual configuration: Requires setup for each new project type or workspace

Transform chaotic employee departures into secure, insightful offboarding experiences - achieving zero security breaches, 100% equipment recovery, and actionable retention insights from every exit interview. What This Workflow Does Revolutionizes employee offboarding with AI-driven exit interview analysis and automated task orchestration: 📝 Exit Interview Capture - Jotform collects resignation details, ratings, feedback, and equipment inventory 🤖 AI Sentiment Analysis - Advanced AI analyzes exit interviews for retention insights, red flags, and patterns ⚠️ Red Flag Detection - Automatically identifies serious issues (harassment, discrimination, ethics) for immediate escalation 👤 Manager Intelligence - Flags management issues and provides coaching recommendations 🔐 Access Revocation - Schedules automatic system access removal on last working day 📦 Equipment Tracking - Generates personalized equipment return checklist for each employee 📚 Knowledge Transfer - Assesses knowledge transfer risk and creates handover plan 💰 Retention Analytics - Identifies preventable departures and competitive intelligence 📧 Automated Notifications - Sends checklists to employees, action items to managers, IT requests 📊 Boomerang Prediction - Calculates likelihood of rehire and maintains alumni relationships Key Features AI Exit Interview Analysis: GPT-4 provides 2+ analytical dimensions including sentiment, preventability, and red flags Preventability Scoring: AI calculates 0-100% score on whether departure was preventable Red Flag Escalation: Automatic detection of harassment, discrimination, ethics, or legal concerns Manager Performance Insights: Identifies management issues requiring coaching or intervention Sentiment Analysis: Analyzes tone, emotions, and overall sentiment from qualitative feedback Competitive Intelligence: Tracks where employees go and what competitors offer Knowledge Transfer Risk Assessment: Evaluates complexity and criticality of knowledge handover Boomerang Probability: Predicts likelihood (0-100%) of employee returning in future Department Trend Analysis: Identifies systemic issues in specific teams or departments Compensation Benchmarking: Flags compensation competitiveness issues Retention Recommendations: AI-generated actionable improvements prioritized by impact Equipment Tracking: Automatic inventory of laptops, phones, cards, and other company property Perfect For Growing Companies: 50-5,000 employees with monthly turnover requiring structured offboarding Tech Companies: Protecting IP and system access with departing engineers and developers Healthcare Organizations: Compliance-critical offboarding with HIPAA and patient data access Financial Services: Regulated industries requiring audit trails and secure access revocation Professional Services: Knowledge-intensive businesses where brain drain is costly Retail & Hospitality: High-turnover environments needing efficient, consistent offboarding Remote-First Companies: Distributed teams requiring coordinated equipment recovery What You'll Need Required Integrations Jotform - Exit interview and resignation form (free tier works) Create your form for free on Jotform using this link OpenAI API - GPT-4 for AI exit interview analysis (~$0.20-0.50 per exit interview) Gmail - Automated notifications to employees, managers, IT, and HR Google Sheets - Exit interview database and retention analytics Quick Start Import Template - Copy JSON and import into n8n Add OpenAI Credentials - Set up OpenAI API key (GPT-4 for best insights) Create Jotform Exit Interview - Build comprehensive form with these sections: Configure Gmail - Add Gmail OAuth2 credentials Setup Google Sheets: Create spreadsheet with "Exit_Interviews" sheet Replace YOUR_GOOGLE_SHEET_ID in workflow Columns will auto-populate on first submission Customization Options AI Prompt Refinement: Tailor analysis for your industry, company culture, and specific concerns Red Flag Categories: Customize what constitutes a red flag for your organization Equipment Types: Add specialized equipment (tools, uniforms, parking passes) Access Systems: Integrate with your specific IT systems for automated revocation Knowledge Transfer Templates: Create role-specific handover checklists Manager Notifications: Add more details based on department or seniority Exit Interview Questions: Add industry-specific or company-specific questions Retention Focus Areas: Adjust AI to focus on specific retention priorities Rehire Workflows: Add automatic alumni network invitations for boomerang candidates Severance Processing: Add nodes for severance agreement generation and tracking Reference Check Process: Include reference policy notifications Benefits COBRA: Automate COBRA benefits notification workflows Expected Results Zero security breaches from lingering access - automated revocation on last day 100% equipment recovery - automated tracking and follow-up 3x faster offboarding - 30 minutes vs 2 hours of manual coordination 85% actionable insights from exit interviews vs 20% with manual reviews 60% improvement in identifying preventable turnover 90% manager compliance with knowledge transfer (vs 40% manual) 50% reduction in repeat management issues through coaching identification 40% increase in boomerang rehires through positive offboarding experience Complete audit trail for legal compliance and investigations Department-level insights identifying systemic retention issues Use Cases Tech Startup (100 Employees, High Growth) Engineer resigns to join competitor. AI detects compensation issue (40% below market), flags manager micromanagement concerns, and identifies preventable departure (preventability: 85%). HR immediately initiates compensation review for engineering team, schedules manager coaching, and retains 3 other engineers considering leaving. Access to codebase revoked automatically on last day. Boomerang probability: 70% - maintains relationship for future recruiting. Healthcare System (500 Nurses) Nurse leaves citing burnout. AI identifies systemic staffing issues in ER department affecting 15% of departures. Flags potential HIPAA violation concern requiring investigation. Automatically revokes EHR access on final day. Equipment recovery (badge, pager, scrubs) tracked with 100% success. Exit insights lead to ER staffing model changes, reducing nurse turnover by 30%. Financial Services Firm Compliance officer departs. AI red flags potential ethics concern requiring immediate investigation. Legal team notified within minutes. Knowledge transfer flagged as "critical risk" due to regulatory expertise. Detailed 30-day handover plan auto-generated. All system access revoked immediately. Complete audit trail maintained for regulatory review. Investigation reveals process gap, not ethical issue, preventing regulatory exposure. Retail Chain (2,000 Employees) Store manager exits. AI aggregates insights across 50 recent retail departures, identifying district manager as common thread (manager rating consistently 2/5). Regional HR intervenes with district manager coaching. Equipment return (keys, registers codes, uniforms) automated via checklist. 95% equipment recovery vs previous 60%. Sentiment trends show seasonal staff prefer flexible scheduling - policy updated chain-wide. Remote Software Company Developer in different timezone resigns. Automated offboarding coordinates across 3 time zones: access revoked at EOD local time, equipment return label emailed internationally, knowledge transfer scheduled with overlap hours. AI detects "career growth" as preventable issue - company implements career ladder framework, reducing senior developer attrition by 45%. Pro Tips Timing Matters: Send Jotform link 1 week before last day for honest feedback (not on exit day) Anonymity Option: Consider anonymous feedback for more candid responses (separate form) Benchmark Scoring: After 50+ exits, calculate your company's average preventability score Manager Patterns: Track exits by manager to identify coaching needs early Department Trends: Monthly reviews of AI insights by department for systemic issues Compensation Data: Cross-reference "compensation issue" flags with market data Boomerang Program: Create formal alumni network for high-probability boomerang candidates Equipment Deposits: Consider requiring deposits for easier equipment recovery Exit Interview Training: Train managers on how to act on AI insights constructively Legal Review: Have legal team review red flag escalation categories quarterly Continuous Improvement: Use AI recommendations to create quarterly retention action plans Stay Interviews: Use exit interview insights to inform "stay interview" questions for current employees Learning Resources This workflow demonstrates advanced n8n automation patterns: AI Agents with complex structured output for multi-dimensional analysis, Sentiment analysis and natural language processing Conditional escalation based on severity and red flags Multi-stakeholder notifications with role-specific messaging Risk assessment algorithms for knowledge transfer and preventability Pattern recognition across qualitative feedback Equipment inventory management with dynamic list generation Compliance automation for access revocation scheduling Predictive analytics for boomerang probability Perfect for learning AI-powered HR automation and organizational analytics! 📊 Workflow Architecture 📝 Jotform Exit Interview Submission ↓ 🧾 Parse Offboarding Data ↓ 🤖 AI Exit Interview Analysis (GPT-4) │ ├─ Retention analysis (preventability scoring) │ ├─ Sentiment analysis (tone, emotions) │ ├─ Manager performance evaluation │ ├─ Department insights │ ├─ Compensation benchmarking │ ├─ Knowledge transfer risk assessment │ ├─ Competitor intelligence │ ├─ Red flag detection │ ├─ Boomerang probability │ └─ Action item generation ↓ 🔗 Extract AI Analysis (JSON) ↓ 🧩 Merge Exit Analysis with Data │ ├─ Calculate days until last day │ ├─ Build equipment checklist │ └─ Assess urgency levels ↓ ⚠️ Has Red Flags? ├─ TRUE → 🚨 Send Red Flag Alert (HR Director/Legal) │ ↓ └─ FALSE → 📧 Send Manager Action Items ↓ ✉️ Send Employee Checklist ↓ 🔐 Send IT Offboarding Request ↓ 📊 Log to Google Sheets Ready to transform employee offboarding? Import this template and turn departures into retention insights while maintaining security and professionalism. Every exit becomes a learning opportunity! 🚪✨ Questions or customization needs? The workflow includes detailed sticky notes explaining each AI analysis component and routing decision.

Zendesk New Ticket → WooCommerce Order Matching, Tagging & Email Automation Automatically enrich Zendesk tickets with WooCommerce order details and reduce manual lookups. This workflow listens for new Zendesk tickets, fetches the ticket requester’s details, retrieves recent WooCommerce orders, matches them by customer email and updates the ticket with order information and relevant tags. If a matching order is marked as completed, it also sends a confirmation email to the customer. Quick Implementation Steps Connect Zendesk, WooCommerce and Gmail credentials in n8n. Import the workflow JSON and review node credentials. Adjust the WooCommerce order fetch limit if needed. Activate the workflow. That’s it — new tickets will now automatically include order context. What It Does This workflow bridges the gap between customer support and order management by linking Zendesk tickets with WooCommerce orders. When a new ticket is created, the workflow retrieves the requester’s profile to identify their email address, which is then used to find related orders in WooCommerce. Because direct email-based filtering is not available in the WooCommerce node, the workflow fetches the latest five orders and performs email matching internally within n8n. This ensures accurate matching while working around platform limitations. Once a matching order is found, the workflow extracts essential details such as order number, status, currency and purchased items. It updates the Zendesk ticket with a private internal note and applies clear order-status-based tags. If the order is marked as completed, the workflow also sends a confirmation email to the customer. Who’s It For Customer support teams using Zendesk and WooCommerce E-commerce businesses handling frequent order-related inquiries Support managers aiming to reduce manual order lookups Teams that want faster, more consistent ticket responses Requirements to Use This Workflow An active Zendesk account with API access A WooCommerce store with REST API credentials A Gmail account (OAuth2) for sending customer emails An active n8n instance Permission to update tickets and users in Zendesk How It Works & How To Set Up Workflow Logic Overview Trigger on New Ticket The workflow starts when a new Zendesk ticket is created with status new. Fetch Ticket Requester Details The requester’s user profile is retrieved to obtain their email address. Fetch Recent WooCommerce Orders The workflow retrieves the latest five orders from WooCommerce. Match Customer Email Each order’s billing email is compared with the Zendesk requester’s email. Only matching orders continue through the workflow. Generate Zendesk Tags Order status is evaluated and mapped to meaningful Zendesk tags. Prepare Ticket Update Payload Order details and tags are formatted for the Zendesk update. Update Zendesk Ticket A private internal note is added to the ticket, along with order-related tags. Check for Completed Orders If the order status is completed, the workflow proceeds to send an email. Send Confirmation Email The customer receives a confirmation email with their order details. Setup Instructions Update credentials in all Zendesk, WooCommerce and Gmail nodes. Review the WooCommerce order fetch limit (default: 5). Verify the email comparison logic in the IF node. Activate the workflow once testing is complete. How To Customize Nodes WooCommerce – Fetch Recent Orders** Increase or decrease the limit value to control how many recent orders are checked. Match Customer Email (IF Node)** Modify comparison logic (for example, make it case-insensitive). Generate Zendesk Tags (Code Node)** Add or change tags based on custom order statuses. Zendesk – Update Ticket** Customize the internal note format or add additional fields. Send Order Confirmation Email** Edit the email content or disable this node if emails are not required. Add-ons (Additional Features) SLA-based ticket prioritization Shipment tracking number enrichment Refund and cancellation detection Slack or Microsoft Teams notifications Extended reporting using Zendesk tags Use Case Examples Automatically attaching order details to “Where is my order?” tickets Speeding up refund or replacement requests Reducing agent time spent switching between Zendesk and WooCommerce Applying consistent order-status tags for analytics Sending proactive confirmation emails for completed orders There are many more possible use cases depending on how this workflow is extended or customized. Troubleshooting Guide | Issue | Possible Cause | Solution | |------|---------------|----------| | No order found | Customer used a different email | Ask the customer to confirm the checkout email | | Wrong order matched | Order not in recent fetch range | Increase the WooCommerce order fetch limit | | No email sent | Order status is not completed | Confirm order status or customize the IF condition | | Ticket not updated | Zendesk permission issue | Verify API credentials and scopes | | Tags missing | Code node not triggered | Check order status logic in the Code node | Need Help? If you need help setting up this workflow, customizing nodes or building additional automation, WeblineIndia is here to support you. Our team specializes in n8n workflow automation, Zendesk integrations and WooCommerce process optimization. Whether you want to extend this workflow or build a similar solution tailored to your business, feel free to reach out to WeblineIndia for expert assistance.

Transform your fleet operations from paper-based chaos to intelligent automation - achieving 40% reduction in breakdowns, 100% inspection compliance, and predictive maintenance that saves thousands in repair costs. What This Workflow Does Revolutionizes fleet management with AI-driven vehicle inspections and predictive maintenance: 📝 Digital Inspections - Jotform captures daily vehicle checks with photos, mileage, and comprehensive checklists 🤖 AI Condition Analysis - Advanced AI Agent evaluates vehicle condition, safety ratings, and maintenance needs ⚠️ Smart Prioritization - Automatically flags critical issues (brakes, safety concerns, DOT compliance) 🔧 Maintenance Routing - Routes issues to appropriate shop teams with detailed work orders 📊 Predictive Maintenance - Tracks mileage thresholds and predicts upcoming service needs ✉️ Automated Notifications - Sends alerts to maintenance teams and confirmation to drivers 📈 Compliance Tracking - Monitors DOT inspections, registrations, and annual certifications 💰 Cost Management - Estimates repair costs and tracks downtime to optimize fleet budget 📋 Complete Documentation - Logs all inspections to Google Sheets for audits and analytics Key Features AI-Powered Vehicle Assessment: GPT-4 analyzes inspection data across 10+ components with safety ratings (0-100) Critical Issue Detection: Automatic identification of safety concerns, DOT violations, and immediate action items Mileage-Based Scheduling: Tracks oil changes, tire rotations, brake inspections with automated reminders Compliance Management: Monitors annual inspections, DOT certifications, and registration expiries Work Order Generation: Creates detailed maintenance orders with instructions, parts needed, and cost estimates Driver Performance Tracking: Evaluates vehicle care quality and identifies training needs Predictive Analytics: Forecasts upcoming maintenance based on usage patterns and vehicle age Emergency Routing: Critical issues trigger immediate alerts to maintenance supervisors Photo Documentation: Captures damage and odometer photos for insurance and warranty claims Real-Time Fleet Status: Tracks operational, out-of-service, and maintenance-required vehicles Cost Estimation: AI-generated repair cost ranges and downtime predictions DOT Audit Ready: Complete inspection logs formatted for regulatory compliance Perfect For Commercial Fleet Operators: Delivery companies, logistics firms managing 10-500+ vehicles Transportation Companies: Trucking fleets requiring DOT compliance and safety standards Service Businesses: Plumbing, HVAC, electrical companies with service vehicle fleets Government Fleets: Municipal vehicles, police departments, public works departments Rental Car Companies: Daily inspections and damage documentation for rental fleets Construction Companies: Heavy equipment and vehicle maintenance tracking Food Delivery Services: High-mileage vehicles requiring frequent inspections Ride-Share Fleet Managers: TNC vehicles needing daily safety checks What You'll Need Required Integrations Jotform - Digital inspection form with photo upload (free tier works) Create your form for free on Jotform using this link OpenAI API - GPT-4 for AI vehicle analysis (~$0.15-0.40 per inspection) Gmail - Automated notifications to maintenance teams and drivers Google Sheets - Inspection database, maintenance tracking, and compliance logs Optional Enhancements Twilio - SMS alerts for critical issues and driver notifications Google Calendar - Automated maintenance scheduling QuickBooks - Expense tracking and repair cost management Fleet Management Software - Integration with Geotab, Samsara, or Fleetio Zapier - Additional integration bridges for specialty systems Google Drive - Photo backup and document storage Maintenance Software - Connect to shop management systems Telematics Integration - Real-time mileage and diagnostics data Quick Start Import Template - Copy JSON and import into n8n Add OpenAI Credentials - Set up OpenAI API key (GPT-4 recommended for accuracy) Create Jotform - Build vehicle inspection form with these essential fields: Driver Info: Name, Email Vehicle Details: Vehicle ID, Make, Model, Year, License Plate Mileage: Current Odometer Reading Fuel Level: Dropdown (Full, 3/4, 1/2, 1/4, Empty) Inspection Checklist: Dropdowns for each component (Good, Fair, Poor, Needs Immediate Attention) Tires Brakes Lights (headlights, taillights, turn signals) Fluid Levels (oil, coolant, brake fluid) Engine Transmission Interior Condition Exterior Condition Issues: Yes/No dropdown + Long text for description Photos: File upload for damage photos and odometer photo Cleanliness Rating: 1-5 star rating Driver Notes: Textarea for additional comments Configure Gmail - Add Gmail OAuth2 credentials for notifications Setup Google Sheets: Create new spreadsheet for fleet tracking Add sheet named "Inspections" Replace YOUR_GOOGLE_SHEET_ID in the workflow Google Sheets will auto-populate columns on first run Customization Options AI Prompt Refinement: Tailor analysis for specific vehicle types (trucks, vans, sedans, heavy equipment) Custom Maintenance Intervals: Adjust service schedules based on manufacturer recommendations Multi-Location Support: Route work orders to different shop locations based on vehicle assignment Priority Escalation: Add manager approval workflows for expensive repairs Driver Training Module: Track recurring issues per driver and generate training recommendations Seasonal Adjustments: Different inspection criteria for winter/summer (tire tread, AC, heating) Vehicle Categories: Separate workflows for passenger vehicles, trucks, specialty equipment Cost Approval Thresholds: Require manager sign-off for repairs over $X amount Parts Inventory Integration: Check parts availability before scheduling maintenance Vendor Management: Route different issue types to specialized vendors Mobile Optimization: Design Jotform specifically for mobile/tablet use in vehicles Offline Mode: Enable Jotform offline submissions for areas with poor connectivity Expected Results 40% reduction in breakdowns - Predictive maintenance catches issues early 100% inspection compliance - Digital tracking eliminates missed checks 24-hour turnaround on maintenance scheduling vs days of manual coordination 30% cost savings - Preventive maintenance avoids expensive emergency repairs 60% faster inspections - Digital forms take 5 minutes vs 15+ for paper Zero lost paperwork - All inspections digitally stored and searchable 85% better DOT audit results - Complete, organized documentation 50% reduction in vehicle downtime - Proactive maintenance scheduling 95% driver compliance - Easy mobile forms increase participation Real-time fleet visibility - Instant status of all vehicles Pro Tips QR Code Access: Place QR codes in each vehicle linking directly to that vehicle's inspection form Pre-Fill Vehicle Data: Use Jotform conditional logic to auto-fill vehicle details when driver enters Vehicle ID Photo Requirements: Make damage and odometer photos mandatory for compliance Daily Reminders: Set up automated daily email/SMS reminders for drivers to complete inspections Seasonal Checklists: Adjust inspection criteria seasonally (winter: tire tread/battery; summer: AC/coolant) Benchmark Analysis: After 100+ inspections, analyze AI accuracy and refine the prompt with real examples Driver Training: Use AI driver performance ratings to identify training needs Telematics Integration: Connect to vehicle GPS/diagnostics for automatic mileage updates Parts Pre-Ordering: Use predictive maintenance to pre-order common parts before needed Maintenance History: Track vehicle-specific patterns (e.g., Vehicle #12 always needs brake work) Incentive Programs: Reward drivers with best vehicle care ratings Mobile-First Design: Ensure Jotform works perfectly on phones - most inspections done on mobile Learning Resources This workflow demonstrates advanced n8n automation: AI Agents with structured JSON output for reliable vehicle assessment Conditional routing based on criticality and safety ratings Database lookups for vehicle maintenance history Predictive analytics using mileage thresholds and time intervals Multi-recipient notifications with role-based messaging Compliance tracking with automatic deadline monitoring Cost estimation algorithms for budget planning Photo handling for documentation and insurance claims Error handling with fallback assessments Perfect for learning fleet operations automation and AI integration! 📊 Workflow Architecture 📝 Jotform Daily Inspection ↓ 🧾 Parse Inspection Data ↓ 📊 Get Vehicle History │ ├─ Last service dates │ ├─ Mileage calculations │ └─ Compliance deadlines ↓ 🤖 AI Fleet Analysis (GPT-4) │ ├─ Condition assessment │ ├─ Safety rating (0-100) │ ├─ Critical issue detection │ ├─ Maintenance recommendations │ ├─ Cost estimation │ ├─ DOT compliance check │ └─ Work order generation ↓ 🔗 Extract & Merge AI Analysis ↓ ⚡ Critical Issue Check ├─ TRUE → 🚨 Critical Alert Email (Maintenance) └─ FALSE → 📋 Routine Report Email (Maintenance) ↓ ✉️ Driver Confirmation Email │ ├─ Inspection received │ ├─ Vehicle status │ ├─ Maintenance scheduled │ └─ Safety notices ↓ 📊 Log to Google Sheets └─ Inspection database └─ Audit trail └─ Analytics data 🔐 Compliance & Security Ready to transform your fleet management? Import this template and eliminate breakdowns, ensure compliance, and save thousands in maintenance costs through AI-powered predictive maintenance! 🚗✨ Questions or customization needs? The workflow includes detailed sticky notes explaining each component and decision point.

Heygen Viral UGC Generation Stop manually creating short-form video content. This n8n workflow automatically generates AI talking-head UGC videos using HeyGen and publishes them to Instagram and Facebook every single day — fully on autopilot. Real reel posted to @pinkmatchaprints — generated and published entirely by this workflow. See It In Action: Real Results from Pink Matcha Watch a live reel generated by this workflow See all published reels on @pinkmatchaprints Every single one of these videos was generated and posted automatically — no recording, no editing, no manual uploading. How It Works The workflow runs every day on a schedule. Here's the full pipeline: Picks today's content from a Google Sheet using a day-of-year rotation across all rows marked Idea Randomly selects a HeyGen talking-photo avatar from a pool of 14 for visual variation across posts Generates a 30-second script using GPT-4.1-mini with a warm, spiritual persona called "The Shepherd" Submits to HeyGen API to render a 720x1280 portrait talking-head video Polls HeyGen every 50 seconds (up to 20 times, ~16 min max) until the video is ready Publishes to Instagram and Facebook via upload-post.com with an auto-generated caption Logs every step to a Production Logs Google Sheet — script, video ID, video URL, and status What You're Getting AI-Generated Scripts with a Defined Persona The workflow uses GPT-4.1-mini with a custom system prompt called "The Shepherd" — a calm, wise, spiritual guide. Every script is: 75 to 90 words (~30 seconds when spoken naturally) Written in warm, grounded, accessible language Tailored to the specific content section from your workbook Always ending with: "Visit the link in bio to learn more." A Content Rotation System That Never Repeats Your content pool lives in a Google Sheet. The workflow selects today's section using day-of-year modulo total rows — so it automatically cycles through your content library without manual scheduling. Add more rows to expand the rotation. Full Production Logging Every execution is tracked row by row in a Production Logs sheet. You can see exactly what ran, what script was used, what the HeyGen video ID is, and what the final status is. Async Video Generation with Smart Polling HeyGen renders videos asynchronously. The workflow handles this gracefully — it submits the job, waits, polls the status API, and routes automatically based on the result: Still processing** → waits and retries Completed** → extracts video URL and posts Failed or timed out** → logs the failure reason to the sheet Dual Platform Publishing Videos are published to both Instagram and Facebook in one execution using upload-post.com. The caption is auto-built from the section title and first 100 characters of the script. Google Sheets Structure The workflow uses a single Google Sheets workbook with two tabs: Tab 1: Workbook Content Your content pool. The workflow reads from this every day. | Column | Description | |---|---| | section_title | Title used in the video caption and script prompt | | workbook_content | Full context for the AI to generate the script from | | key_message | The core takeaway — passed to the AI as emphasis | | Status | Set to Idea to include in rotation. Any other value skips the row. | Tab 2: Production Logs Auto-populated. Do not edit manually. | Column | Description | |---|---| | Date | Date the video was generated | | Section Title | Which content section was used | | Script Text | The generated script | | HeyGen Video ID | Used to poll render status | | Raw Video URL | Direct HeyGen video URL | | Final Video URL | Final URL passed to the publishing step | | Status | Lifecycle status or failure reason | Setup Guide Step 1: Credentials | Service | Credential Type | Where Used | |---|---|---| | HeyGen | HTTP Header Auth (X-Api-Key) | Generate and poll videos | | upload-post.com | HTTP Header Auth | Post to Instagram / Facebook | | Google Sheets | OAuth2 | Read content, write logs | | OpenAI | API Key | Generate scripts (GPT-4.1-mini) | Step 2: Google Sheets Duplicate the workbook structure described above Replace the documentId in all Google Sheets nodes with your spreadsheet ID Populate the Workbook Content tab and set Status = Idea on each row Step 3: HeyGen Avatars The workflow ships with 14 hardcoded talking-photo avatar IDs in the Code: Select Random Avatar & Combine Data node. Replace these with your own HeyGen avatar IDs from your HeyGen account. Step 4: upload-post.com Account Update the user field in both the facebook and instagram nodes from pink-matcha to your upload-post.com username. Step 5: Enable Facebook (When Ready) The facebook node is disabled by default. Once you confirm Instagram is posting correctly, enable it in n8n. Step 6: Adjust the Schedule The trigger is set to fire daily at 12pm noon. The node name says "9am" — rename or adjust the time in the Schedule node to match your target posting time. Technical Specs Script AI**: OpenAI GPT-4.1-mini Video Generation**: HeyGen v2 API (talking-photo avatars) Video Format**: 720x1280 portrait (Reels / Shorts) Polling**: 50-second intervals, max 20 attempts (~16 min timeout) Publishing**: upload-post.com multipart API Logging**: Google Sheets (OAuth2) Error Workflow**: Separate n8n error workflow for unexpected crashes Platform**: n8n (self-hosted or cloud) Frequently Asked Questions Q: Do I need a HeyGen subscription? A: Yes. You need a HeyGen account with API access and at least one talking-photo avatar created in your account. Q: What is upload-post.com? A: A third-party service that handles posting video content to Instagram and Facebook on your behalf. You need an account connected to your social profiles. Q: Can I change the AI persona? A: Yes. Edit the system prompt in the Generate Script node to use any persona, tone, or style you want. Q: Can I post to TikTok or YouTube Shorts? A: upload-post.com supports other platforms. You can add additional HTTP request nodes following the same pattern as the instagram node and change the platform[] parameter. Q: How do I add more content? A: Add rows to the Workbook Content tab in Google Sheets and set Status = Idea. The rotation automatically picks them up. Q: What happens if HeyGen fails? A: The workflow detects failure or timeout and writes the reason to the Production Logs sheet. A separate n8n error workflow handles unexpected crashes. Q: How long does one execution take? A: Typically 5 to 15 minutes end-to-end, depending on HeyGen render time. Join the Community Other free n8n workflows GitHub repository Join AI + Automation Discord Official website Live example account Powered by n8n • OpenAI • HeyGen • Google Sheets • upload-post.com

Jotform AI-Powered Loan Application & Pre-Approval Automation System Transform manual loan processing into same-day pre-approvals - achieving 50% faster closings, 90% reduction in manual review time, and automated underwriting decisions with AI-powered financial analysis and instant applicant notifications. What This Workflow Does Revolutionizes mortgage and loan processing with AI-driven financial analysis and automated decision workflows: 📝 Digital Application Capture - Jotform collects complete applicant data, income, employment, and loan details 🤖 AI Financial Analysis - GPT-4 calculates debt-to-income ratio, loan-to-value ratio, and approval likelihood 💳 Automated Credit Assessment - Instant credit score evaluation and payment history analysis 📊 Risk Scoring - AI assigns 1-100 risk scores based on multiple financial factors ✅ Intelligent Routing - Automatic pre-approval, conditional approval, or denial based on lending criteria 📧 Instant Notifications - Applicants receive approval letters within minutes of submission 👔 Underwriter Alerts - Pre-approved loans automatically route to loan officers with complete analysis 📋 Document Tracking - Required documents list generated based on application specifics 📅 Closing Scheduling - Approved loans trigger closing coordination workflows 📈 Complete Audit Trail - Every application logged with financial metrics and decision rationale Key Features AI Underwriting Analyst: GPT-4 evaluates loan applications across 10+ financial dimensions including debt ratios, risk assessment, and approval recommendations Debt-to-Income Calculation: Automatically calculates DTI ratio and compares against lending standards (43% threshold for qualified mortgages) Loan-to-Value Analysis: Evaluates down payment adequacy and property value against loan amount requested Credit Score Integration: Simulated credit assessment (ready for real credit bureau API integration like Experian, Equifax, TransUnion) Approval Likelihood Scoring: AI predicts approval probability as high/medium/low based on complete financial profile Risk Assessment: 1-100 risk score considers income stability, debt levels, credit history, and employment status Interest Rate Recommendations: AI suggests appropriate rate ranges based on applicant qualifications Conditional Approval Logic: Identifies specific requirements needed for final approval (additional documentation, debt paydown, etc.) Multi-Path Routing: Different workflows for pre-approved (green path), conditional (yellow path), and denied (red path) applications Monthly Payment Estimates: AI calculates estimated mortgage payments including principal, interest, taxes, and insurance Employment Verification Tracking: Flags employment status and stability in approval decision Document Requirements Generator: Custom list of required documents based on applicant situation and loan type Underwriter Dashboard Integration: Pre-approved applications automatically notify underwriters with complete financial summary Applicant Communication: Professional, branded emails for every outcome (pre-approval, conditional, denial) Alternative Options for Denials: Denied applicants receive constructive guidance on improving qualifications Compliance Ready: Decision rationale documented for regulatory compliance and audit requirements Perfect For Mortgage Lenders: Banks and credit unions processing home loan applications (purchase, refinance, HELOC) Commercial Lenders: Business loan and commercial real estate financing institutions Auto Finance Companies: Car dealerships and auto loan providers needing instant credit decisions Personal Loan Providers: Fintech companies and online lenders offering consumer loans Credit Unions: Member-focused financial institutions streamlining loan approval processes Mortgage Brokers: Independent brokers managing applications for multiple lenders Hard Money Lenders: Alternative lenders with custom underwriting criteria Student Loan Services: Educational financing with income-based qualification What You'll Need Required Integrations Jotform - Loan application form (free tier works, Pro recommended for file uploads) Create your form for free on Jotform using this link: https://www.jotform.com OpenAI API - GPT-4 for AI financial analysis and underwriting decisions (approximately 0.30-0.50 USD per application) Gmail - Automated notifications to applicants and underwriters Google Sheets - Loan application database and pipeline tracking Optional Integrations (Recommended for Production) Credit Bureau APIs - Experian, Equifax, or TransUnion for real credit pulls Document Management - DocuSign, HelloSign for e-signatures and document collection Property Appraisal APIs - Automated valuation models for property verification Calendar Integration - Calendly or Google Calendar for closing date scheduling CRM Systems - Salesforce, HubSpot for lead management and follow-up Loan Origination Software (LOS) - Encompass, Calyx, BytePro integration Quick Start Import Template - Copy JSON and import into n8n Add OpenAI Credentials - Set up OpenAI API key (GPT-4 required for accurate underwriting) Create Jotform Loan Application: Full Name (q3_fullName) Email (q4_email) Phone (q5_phone) Social Security Number (q6_ssn) - encrypted field Monthly Income (q7_monthlyIncome) - number field Monthly Debts (q8_monthlyDebts) - number field (credit cards, car loans, student loans) Loan Amount Requested (q9_loanAmount) - number field Down Payment (q10_downPayment) - number field Property Value (q11_propertyValue) - number field Employment Status (q12_employmentStatus) - dropdown (Full-time, Part-time, Self-employed, Retired) Additional fields: Date of Birth, Address, Employer Name, Years at Job, Property Address Configure Gmail - Add Gmail OAuth2 credentials (same for all 4 Gmail nodes) Setup Google Sheets: Create spreadsheet with "Loan_Applications" sheet Replace YOUR_GOOGLE_SHEET_ID in workflow 16 columns auto-populate: timestamp, applicationId, applicantName, email, phone, loanAmount, downPayment, monthlyIncome, monthlyDebts, creditScore, dtiRatio, ltvRatio, riskScore, approvalStatus, monthlyPayment, interestRate Customize Approval Criteria (Optional): Edit "Check Approval Status" node Adjust credit score minimum (default: 680) Modify DTI threshold (default: 43%) Set LTV requirements Configure Credit Integration: Replace "Simulate Credit Check" node with real credit bureau API Or keep simulation for testing/demo purposes Brand Email Templates: Update company name, logo, contact information Customize approval letter formatting Add compliance disclosures as required Set Underwriter Email: Update underwriter contact in "Notify Underwriter" node Add CC recipients for loan ops team Test Workflow - Submit test applications with different scenarios: High income, low debt (should pre-approve) Moderate income, high debt (should conditional) Low income, excessive debt (should deny) Compliance Review - Have legal/compliance team review automated decision logic Go Live - Deploy form on website, share with loan officers, integrate with marketing Customization Options Loan Type Variations: Customize for conventional, FHA, VA, USDA, jumbo, or commercial loans Custom Underwriting Rules: Adjust DTI limits, credit minimums, LTV requirements per loan product Manual Review Triggers: Flag edge cases for manual underwriter review before automation Document Upload Integration: Add Jotform file upload fields for paystubs, tax returns, bank statements Income Verification APIs: Integrate with Plaid, Finicity, or Argyle for automated income verification Employment Verification: Connect to The Work Number or other employment databases Property Appraisal Automation: Integrate AVMs (Automated Valuation Models) from CoreLogic, HouseCanary Co-Borrower Support: Add fields and logic for joint applications with multiple income sources Business Loan Customization: Modify for business financials (revenue, EBITDA, business credit scores) Rate Shopping: Integrate rate tables to provide real-time interest rate quotes Pre-Qualification vs Pre-Approval: Create lighter version for soft credit pull pre-qualification Conditional Approval Workflows: Automated follow-up sequences for document collection Closing Coordination: Integrate with title companies, attorneys, closing services Regulatory Compliance: Add TRID timeline tracking, adverse action notices, HMDA reporting Multi-Language Support: Translate forms and emails for Spanish, Chinese, other languages Expected Results Same-day pre-approval - Applications processed in minutes vs 3-5 days manual review 50% faster closings - Streamlined process reduces time from application to closing 90% reduction in manual review time - AI handles initial underwriting, humans only review exceptions 95% applicant satisfaction - Instant decisions and clear communication improve experience 75% reduction in incomplete applications - Required fields force complete submission 60% fewer applicant calls - Automated status updates reduce "where's my application" inquiries 100% application tracking - Complete audit trail from submission to final decision 40% increase in loan officer productivity - Focus on high-value activities, not data entry 80% decrease in approval errors - Consistent AI analysis eliminates human calculation mistakes 30% improvement in compliance - Automated documentation and decision rationale for audits Pro Tips Test with Multiple Scenarios: Submit applications with various income/debt combinations to validate routing logic works correctly Adjust DTI Thresholds for Loan Type: Conventional mortgages: 43% max. FHA loans: 50% max. Auto loans: 35-40% max. Personal loans: 40-45% max. Credit Score Tiers Matter: Build rate sheets with score tiers (740+: prime, 680-739: near-prime, 620-679: subprime, below 620: denied or hard money) Income Verification Priorities: W-2 employees (easy), self-employed (complex), commission/bonus heavy (average 2 years), rental income (75% counts), gig economy (difficult) Document Checklist Customization: Vary required docs by loan type, amount, and risk profile to avoid over-documentation for low-risk loans Conditional Approval vs Outright Denial: When in doubt, use conditional - gives applicants path to approval and keeps them in pipeline Adverse Action Notices: For denials, include specific reasons (per FCRA requirements) and instructions for disputing credit report errors Pre-Qualification vs Pre-Approval: Pre-qual uses soft credit pull (no impact on score), pre-approval uses hard pull (official decision) Co-Borrower Logic: When DTI is high, automatically suggest co-borrower as option to strengthen application Rate Lock Automation: Pre-approved applications should include rate lock expiration date (typically 30-60 days) Property Appraisal Triggers: Auto-order appraisals for pre-approved mortgage applications to keep process moving Underwriter Dashboard: Build Google Sheets dashboard with filters for underwriters to sort by approval status, loan amount, date Compliance Monitoring: Regular audits of AI decisions to ensure no discriminatory patterns (disparate impact analysis) Customer Service Integration: Link application IDs to support tickets so agents can quickly pull up loan status Marketing Attribution: Track lead sources in form to measure which marketing channels produce best-quality applicants Learning Resources This workflow demonstrates advanced automation: AI Agents for Financial Analysis: Multi-dimensional loan qualification using BANT-style underwriting criteria Complex Conditional Logic: Multi-path routing with nested IF conditions for approval/conditional/denial workflows Financial Calculations: Automated DTI, LTV, DSCR, and payment estimation algorithms Risk Scoring Models: Comprehensive risk assessment combining credit, income, debt, and employment factors Decision Documentation: Complete audit trail with AI reasoning for regulatory compliance Email Customization: Dynamic content generation based on approval outcomes and applicant situations Data Pipeline Design: Structured data flow from application through analysis to decision and notification Simulation vs Production: Credit check node designed for easy swap from simulation to real API integration Parallel Processing: Simultaneous logging and notification workflows for efficiency Workflow Orchestration: Coordination of multiple decision points and communication touchpoints Questions or customization? The workflow includes detailed sticky notes explaining each analysis component and decision logic. Template Compatibility ✅ n8n version 1.0+ ✅ Works with n8n Cloud and Self-Hosted ✅ Production-ready for financial institutions ✅ Fully customizable for any loan type Compliance Note: This template is designed for demonstration and automation purposes. Always consult with legal counsel to ensure compliance with TILA, RESPA, ECOA, FCRA, and applicable state lending regulations before deploying in production.

Software Vulnerability Tracker with Pushover and Notion ⚠️ COMMUNITY TEMPLATE DISCLAIMER: This is a community-contributed template that uses ScrapeGraphAI (a community node). Please ensure you have the ScrapeGraphAI community node installed in your n8n instance before using this template. This workflow automatically scans multiple patent databases on a weekly schedule, filters new filings relevant to selected technology domains, saves the findings to Notion, and pushes instant alerts to your mobile device via Pushover. It is ideal for R&D teams and patent attorneys who need up-to-date insights on emerging technology trends and competitor activity. Pre-conditions/Requirements Prerequisites An n8n instance (self-hosted or n8n cloud) ScrapeGraphAI community node installed Active Notion account with an integration created Pushover account (user key & application token) List of technology keywords / CPC codes to monitor Required Credentials ScrapeGraphAI API Key** – Enables web scraping of patent portals Notion Credential** – Internal Integration Token with database write access Pushover Credential** – App Token + User Key for push notifications Additional Setup Requirements | Service | Needed Item | Where to obtain | |---------|-------------|-----------------| | USPTO, EPO, WIPO, etc. | Public URLs for search endpoints | Free/public | | Notion | Database with properties: Title, Abstract, URL, Date | Create in Notion | | Keyword List | Text file or environment variable PATENT_KEYWORDS | Define yourself | How it works This workflow automatically scans multiple patent databases on a weekly schedule, filters new filings relevant to selected technology domains, saves the findings to Notion, and pushes instant alerts to your mobile device via Pushover. It is ideal for R&D teams and patent attorneys who need up-to-date insights on emerging technology trends and competitor activity. Key Steps: Schedule Trigger**: Fires every week (default Monday 08:00 UTC). Code (Prepare Queries)**: Builds search URLs for each keyword and data source. SplitInBatches**: Processes one query at a time to respect rate limits. ScrapeGraphAI**: Scrapes patent titles, abstracts, links, and publication dates. Code (Normalize & Deduplicate)**: Cleans data, converts dates, and removes already-logged patents. IF Node**: Checks whether new patents were found. Notion Node**: Inserts new patent entries into the specified database. Pushover Node**: Sends a concise alert summarizing the new filings. Sticky Notes**: Document configuration tips inside the workflow. Set up steps Setup Time: 10-15 minutes Install ScrapeGraphAI: In n8n, go to “Settings → Community Nodes” and install @n8n-nodes/scrapegraphai. Add Credentials: ScrapeGraphAI: paste your API key. Notion: add the internal integration token and select your database. Pushover: provide your App Token and User Key. Configure Keywords: Open the first Code node and edit the keywords array (e.g., ["quantum computing", "Li-ion battery", "5G antenna"]). Point to Data Sources: In the same Code node, adjust the sources array if you want to add/remove patent portals. Set Notion Database Mapping: In the Notion node, map properties (Name, Abstract, Link, Date) to incoming JSON fields. Adjust Schedule (optional): Double-click the Schedule Trigger and change the CRON expression to your preferred interval. Test Run: Execute the workflow manually. Confirm that the Notion page is populated and a Pushover notification arrives. Activate: Switch the workflow to “Active” to enable automatic weekly execution. Node Descriptions Core Workflow Nodes: Schedule Trigger** – Defines the weekly execution time. Code (Build Search URLs)** – Dynamically constructs patent search URLs. SplitInBatches** – Sequentially feeds each query to the scraper. ScrapeGraphAI** – Extracts patent metadata from HTML pages. Code (Normalize Data)** – Formats dates, adds UUIDs, and checks for duplicates. IF** – Determines whether new patents exist before proceeding. Notion** – Writes new patent records to your Notion database. Pushover** – Sends real-time mobile/desktop notifications. Data Flow: Schedule Trigger → Code (Build Search URLs) → SplitInBatches → ScrapeGraphAI → Code (Normalize Data) → IF → Notion & Pushover Customization Examples Change Notification Message // Inside the Pushover node "Message" field return { message: 📜 ${items[0].json.count} new patent(s) detected in ${new Date().toDateString()}, title: '🆕 Patent Alert', url: items[0].json.firstPatentUrl, url_title: 'Open first patent' }; Add Slack Notification Instead of Pushover // Replace the Pushover node with a Slack node { text: ${$json.count} new patents published:\n${$json.list.join('\n')}, channel: '#patent-updates' } Data Output Format The workflow outputs structured JSON data: { "title": "Quantum Computing Device", "abstract": "A novel qubit architecture that ...", "url": "https://patents.example.com/US20240012345A1", "publicationDate": "2024-06-01", "source": "USPTO", "keywordsMatched": ["quantum computing"] } Troubleshooting Common Issues No data returned – Verify that search URLs are still valid and the ScrapeGraphAI selector matches the current page structure. Duplicate entries in Notion – Ensure the “Normalize Data” code correctly checks for existing URLs or IDs before insert. Performance Tips Limit the number of keywords or schedule the workflow during off-peak hours to reduce API throttling. Enable caching inside ScrapeGraphAI (if available) to minimize repeated requests. Pro Tips: Use environment variables (e.g., {{ $env.PATENT_KEYWORDS }}) to manage keyword lists without editing nodes. Chain an additional “HTTP Request → ML Model” step to auto-classify patents by CPC codes. Create a Notion view filtered by publicationDate is within past 30 days for quick scanning.

Description Automate LinkedIn organization page posting with precise time scheduling and Google Drive media management. Runs hourly during business hours, processes approved posts scheduled for today, waits until exact time, publishes to LinkedIn, and updates tracking sheet—perfect for maintaining consistent LinkedIn presence. What this workflow does This workflow automates LinkedIn organization page posting with precise timing control and a centralized Google Sheets content calendar. It runs four times daily (9:45 AM, 10:45 AM, 11:45 AM, 12:45 PM) and reads your Google Sheet to find posts marked with Approval Status = "Good" and Platform = "LinkedIn" that are scheduled for today. Unlike batch processing workflows, this processes ONE post per run to prevent duplicate scheduling. Once it finds a post, it marks it as "Scheduled" in your sheet, then uses a Wait node to pause the workflow execution until the exact scheduled time (with automatic timezone conversion from Eastern to India time). At the scheduled moment, it either downloads an image from Google Drive and publishes a Creative Post, or publishes an Article link—depending on the Post Type. After successfully publishing to your LinkedIn organization page, it updates your Google Sheet with the live post URL and changes the Approval Status to "Published", creating a complete audit trail with precise timing control. Perfect for social media managers maintaining consistent LinkedIn presence, marketing agencies scheduling client LinkedIn content with approval checkpoints, content creators batch-planning professional posts, and teams needing collaborative LinkedIn calendars with exact time control. Key features Precise time scheduling with Wait node: Unlike immediate publishing, this workflow uses n8n's Wait node to pause execution until the exact scheduled time—ensuring posts publish at 10:00 AM sharp, not 9:45 AM when the workflow runs. Timezone conversion included: Automatically converts "Scheduled On" times from Eastern Time (content creator's timezone) to India Time (server timezone) using DateTime.fromFormat—no manual calculation needed. One post per run: Processes only the first pending post each time the workflow runs, preventing duplicate scheduling if multiple posts share the same time slot—ensures clean execution and tracking. Dual post type support: Handles both Creative Posts (image posts downloaded from Google Drive) and Articles (link posts with article URL)—automatically routes based on Post Type column. LinkedIn organization posting: Posts directly to your LinkedIn organization/company page (not personal profile) using LinkedIn Community Management API with proper OAuth authentication. Status progression tracking: Three-stage workflow: Good (approved) → Scheduled (waiting for time) → Published (live on LinkedIn)—always know what's queued vs. what's live. Google Sheets content calendar: Manage LinkedIn posts in a familiar spreadsheet with columns for Scheduled On, Platform, Post Type, Caption, Media URL, and Approval Status—no complex tools needed. Google Drive media integration: Stores images in Google Drive (centralized storage), then automatically downloads them when publishing Creative Posts—supports shared drives and private files. Post URL tracking: After publishing, updates Google Sheet with the live LinkedIn post URL (https://www.linkedin.com/feed/update/{urn})—enables easy performance tracking and reporting. Runs during business hours only: Schedule trigger fires at :45 minutes (9-12 AM) to catch morning posts—won't run at night or on weekends unless you modify the cron. How it works 1. Hourly trigger during business hours A cron trigger runs at 9:45 AM, 10:45 AM, 11:45 AM, and 12:45 PM every day. The :45 timing gives a 15-minute buffer before the hour to schedule posts for :00, :15, :30, or :45 times. 2. Load LinkedIn credentials The workflow reads a separate ".env" sheet in your Google Sheets document containing: LinkedIn Organization ID:** Your company page's unique ID (e.g., 56420402) Other LinkedIn-specific configuration This centralizes credentials so multiple workflows can share the same settings. 3. Fetch approved LinkedIn posts The workflow reads your main "Post URL" sheet and applies two filters: Approval Status = "Good":** Only processes approved posts Platform = "LinkedIn":** Filters out Facebook, Instagram, etc. This returns all approved LinkedIn posts regardless of date. 4. Filter posts scheduled for today A Code node compares the "Scheduled On" column value against today's date (ignores time, just checks the date part). Only posts scheduled for today pass through. Supported date format: "2025-10-30 10:00" (YYYY-MM-DD HH:MM) Time uses 24-hour format 5. Process first post only Critical difference from Facebook workflow: A Code node extracts only the FIRST item from the filtered posts. Why only one post? Prevents duplicate scheduling if workflow runs multiple times Wait node works on single execution paths Ensures precise timing per post Next run will pick up the next pending post 6. Route by post type A Switch node checks the Post Type column: Output 0 (Creative Post):** Posts with images (routes to Branch A) Output 1 (Article):** Posts with article links (routes to Branch B) Branch A: Creative Post with Image 7a. Mark as scheduled in sheet Before waiting, the workflow updates the Google Sheet: Approval Status:** "Scheduled" This prevents the same post from being picked up again in the next hourly run. 8a. Wait until scheduled time Most critical node: The Wait node pauses workflow execution until the exact scheduled time. Timezone conversion logic: DateTime.fromFormat( $('Route by Post Type').item.json['Scheduled On'], 'yyyy-MM-dd HH-mm', { zone: 'America/New_York' } // Input timezone (Eastern) ) .setZone('Asia/Kolkata') // Server timezone (India) .toFormat("yyyy-MM-dd'T'HH:mm:ss") Example: Sheet value: "2025-10-30 14:00" (Eastern Time) Converted to: "2025-10-30T23:30:00" (India Time) Workflow resumes at exactly 11:30 PM India time = 2:00 PM Eastern 9a. Prepare post data Aggregate node (keeps data structure intact for next nodes). 10a. Download image from Google Drive Uses the Media URL (Google Drive sharing link) to download the image file. Supports: Direct Google Drive file URLs Shared Drive files Public or private files (as long as OAuth account has access) The image is downloaded as binary data. 11a. Publish creative post to LinkedIn Uses n8n's LinkedIn node with these settings: Authentication:** communityManagement (LinkedIn Community Management API) Post as:** organization Organization:** 56420402 (your company page ID) Text:** Caption from Google Sheet Share media category:** IMAGE Binary data:** Downloaded image The LinkedIn API returns a URN (unique post identifier). 12a. Save post URL & mark published Constructs the LinkedIn post URL: https://www.linkedin.com/feed/update/{urn} Then updates the Google Sheet row: Approval Status:** "Published" Post URL:** Constructed LinkedIn URL Branch B: Article Post with Link 7b. Mark article as scheduled Updates Google Sheet: Approval Status = "Scheduled" 8b. Wait until article scheduled time Same Wait node logic as Creative Posts—pauses until exact scheduled time with timezone conversion. 9b. Prepare article data Aggregate node. 10b. Publish article link to LinkedIn Uses LinkedIn node with these settings: Authentication:** communityManagement Post as:** organization Organization:** 56420402 Text:** Caption from Google Sheet Share media category:** ARTICLE Original URL:** Media URL (the article link) LinkedIn scrapes the article URL and creates a rich preview card. 11b. Save article URL & mark published Same as Creative Posts—constructs post URL and updates sheet with "Published" status. Setup requirements Tools you'll need: Active n8n instance (self-hosted or n8n Cloud) Google Sheets with OAuth access Google Drive with OAuth access LinkedIn organization/company page (not personal profile) LinkedIn Community Management API credentials Estimated setup time: 35–40 minutes Configuration steps 1. Set up LinkedIn Community Management API Go to LinkedIn Developer Console Create an app (or use existing) Add "Community Management API" product Request access for your organization page Under Auth → OAuth 2.0 settings: Add redirect URL: https://your-n8n-instance.com/rest/oauth2-credential/callback Note your Client ID and Client Secret In n8n: Credentials → Add credential → LinkedIn Community Management OAuth2 API Complete OAuth flow and select your organization page 2. Find your LinkedIn Organization ID Method 1 (URL): Go to your LinkedIn company page URL will be: https://www.linkedin.com/company/{company-name}/ View page source and search for "organizationId" Copy the numeric ID (e.g., 56420402) Method 2 (API): Use LinkedIn API endpoint: /v2/organizationalEntityAcls?q=roleAssignee Find your organization in the response 3. Set up Google Sheets Create two sheets in one Google Sheets document: Sheet 1: ".env" (credentials) | LinkedIn Organization ID | |---| | 56420402 | Sheet 2: "Post URL" (content calendar) | Scheduled On | Platform | Post Type | Caption | Media URL | Approval Status | Post URL | row_number | |---|---|---|---|---|---|---|---| | 2025-10-30 10:00 | LinkedIn | Creative Post | Excited to announce our new product! | https://drive.google.com/file/d/xxx | Good | | 1 | | 2025-10-30 14:00 | LinkedIn | Article | Check out our latest blog post | https://blog.example.com/post | Good | | 2 | Important column details: Scheduled On:** Format YYYY-MM-DD HH-MM (24-hour, Eastern Time) Platform:** Must be "LinkedIn" (case-sensitive) Post Type:** "Creative Post" (with image) or "Article" (with link) Caption:** Post text (LinkedIn supports up to 3,000 characters) Media URL:** Google Drive URL for Creative Post, article URL for Article Approval Status:** "Good" (publish), "Pending" (hold), "Rejected" (skip) Post URL:** Leave empty (auto-filled after publishing) row_number:** Auto-generated by Google Sheets 4. Connect Google Sheets OAuth In n8n: Credentials → Add credential → Google Sheets OAuth2 API Complete OAuth authentication Update these nodes with your sheet URL: "Load LinkedIn Organization Credentials" → .env sheet "Fetch Approved LinkedIn Posts" → Post URL sheet "Mark as Scheduled in Sheet" → Post URL sheet "Save Post URL & Mark Published" → Post URL sheet "Mark Article as Scheduled" → Post URL sheet "Save Article URL & Mark Published" → Post URL sheet 5. Connect Google Drive OAuth In n8n: Credentials → Add credential → Google Drive OAuth2 API Complete OAuth authentication Open "Download Image from Google Drive" node Select your Google Drive credential 6. Update LinkedIn Organization ID Open these nodes and replace "56420402" with your organization ID: "Publish Creative Post to LinkedIn"** "Publish Article Link to LinkedIn"** 7. Adjust timezone (if needed) If your content calendar uses a different timezone than Eastern: Open "Wait Until Scheduled Time" node Change { zone: 'America/New_York' } to your timezone Common values: 'America/Los_Angeles' (Pacific), 'UTC', 'Europe/London' If your n8n server is not in India Time: Change .setZone('Asia/Kolkata') to your server's timezone 8. Test the workflow Add a test post scheduled for 5 minutes from now Set Platform = "LinkedIn", Post Type = "Creative Post", Approval Status = "Good" Manually trigger the workflow (or wait for next hourly run) Verify: Sheet updated to "Scheduled" Workflow execution shows as "Waiting" in n8n At scheduled time, post publishes to LinkedIn Sheet updated to "Published" with URL 9. Activate the workflow Toggle the workflow to Active The workflow will now run automatically 4 times daily Check your LinkedIn page to verify posts are publishing correctly Use cases Social media managers: Schedule 15-20 LinkedIn posts per week from one Google Sheet. Team members add content, you approve, workflow handles precise timing and publishing—no manual LinkedIn.com logins. B2B marketing teams: Maintain consistent LinkedIn company page presence with thought leadership articles, product updates, and team highlights. Schedule weeks in advance, let automation publish at optimal times. Content creators: Batch-create LinkedIn content on Mondays, schedule throughout the week with precise timing. Focus on creation, not distribution—workflow handles publishing. Agencies managing client pages: One Google Sheet per client, separate workflows per organization ID. Centralized content calendar with approval workflow before posting to client pages. Recruiting teams: Schedule hiring posts, culture updates, and employee spotlights to maintain active company presence. Track all post URLs for performance analysis. Personal brands using company pages: If you manage a LinkedIn company page for your personal brand or business, schedule promotional content, case studies, and announcements with professional timing control. Customization options Process multiple posts per run Change "Process First Post Only" node logic: Current: Returns only item 0 Modified: Return all items (use Loop node to process sequentially) Note: Wait nodes won't work with loops—consider using scheduled_publish_time if LinkedIn API supports it Change scheduling frequency Edit the "Run Every Hour" cron expression: Current: 45 9-12 * * * (9:45-12:45 AM hourly) All day: 45 * * * * (every hour at :45) Business hours extended: 45 9-17 * * 1-5 (9 AM-5 PM, Monday-Friday) Twice daily: 0 9,15 * * * (9:00 AM and 3:00 PM) Add video post support LinkedIn supports video posts via Community Management API: Add "Post Type" = "Video" Download video from Google Drive (instead of image) Change share media category to VIDEO Upload video to LinkedIn media upload endpoint first, then create post Support personal profiles If you want to post to personal profiles (not organization): Change authentication from "communityManagement" to "oAuth2" Change "Post as" from "organization" to "person" Use LinkedIn OAuth2 API credentials (not Community Management) Add Slack notifications After publishing nodes, add: Slack node** to send confirmation Format:** "Published LinkedIn post: [URL] at [time]" Include:** Post caption preview for context Multi-organization support Modify .env sheet to support multiple company pages: | Organization Name | LinkedIn Organization ID | |---|---| | Main Brand | 56420402 | | Sub Brand | 78901234 | Add "Organization Name" column to Post URL sheet, then filter and route by organization. Troubleshooting Posts not publishing OAuth expired:** Re-authenticate LinkedIn Community Management API credentials in n8n. Organization permissions:** Verify your LinkedIn account has admin/content creator role on the organization page. API access:** Ensure your LinkedIn app has Community Management API product added and approved. Organization ID wrong:** Double-check the ID matches your actual company page. Wait node not working Scheduled time in past:** n8n Wait node requires future times. If "Scheduled On" is in the past when workflow runs, it fails. Ensure posts are scheduled for future times only. Timezone mismatch:** If posts publish at wrong times, verify timezone conversion is correct (Eastern → your server timezone). DateTime format error:** Ensure "Scheduled On" uses exactly "YYYY-MM-DD HH-MM" format with space between date and time. Images not downloading from Google Drive OAuth expired:** Re-authenticate Google Drive credentials. File permissions:** Ensure the Google account connected to n8n has "Viewer" access to Drive files. Sharing link format:** Media URL must be full Google Drive URL (https://drive.google.com/file/d/FILE_ID/view), not shortened. Multiple posts with same time Current limitation:** This workflow processes ONE post per run. If multiple posts share the same scheduled time, only the first will publish. Solution:** Stagger times by 1 minute (10:00, 10:01, 10:02) or modify workflow to process multiple posts. Sheet not updating OAuth expired:** Re-authenticate Google Sheets credentials. Sheet name mismatch:** Verify sheet tab name is exactly "Post URL" and ".env" (case-sensitive). row_number missing:** Ensure the sheet has a row_number column auto-generated by formula: =ROW()-1 Article previews not showing URL not accessible:** LinkedIn needs to scrape the article URL. Ensure it's publicly accessible (not behind login/paywall). No Open Graph tags:** Article pages need Open Graph meta tags (og:title, og:description, og:image) for LinkedIn to generate previews. LinkedIn cache:** Sometimes LinkedIn caches old previews. Use LinkedIn Post Inspector to refresh cache. Resources n8n documentation LinkedIn Community Management API LinkedIn OAuth Guide Google Sheets API Google Drive API n8n Wait node n8n LinkedIn node Support Need help or custom development? 📧 Email: info@isawow.com 🌐 Website: https://isawow.com/